1 Introduction


After more than two decades of economic growth and relative progress, the beginning of the 70s decade signaled an inauspicious reversal: after a sustained period of rising budget deficits and high interest rate following the Johnson administration, Nixon’s economic team started a massive upheaval in American, and by extension, global economic policy. This era of upheaval, otherwise inconspicuous to those outside of macroeconomic and political economy circles, would come to be known as the Nixon shock: the post-war economic system set up around the US as the world’s primary gold reserve and by extension, around the US Dollar as the world’s reserve currency was rapidly undone. The world after 1971 was poised to usher in an era radically different from its predecessor.

This demonstration’s purpose is to showcase the year 1971 and the following years as a watershed moment, both for domestic economy of the US and for the global economic system. Through RStudio’s data aggregation, manipulation, and visualisation suites and using publicly available economic data, I will be examining several key performance indices that might help us glean some insight into the post-Bretton Woods world order.

It is important to note that this is not a policy or economic paper, nor will it attempt to delve deeply into the macroeconomic theory behind the Nixon shock or other developments in the 1970s. Such an analysis would fall beyond my expertise as a data visualisation enthusiast and political economy student. Whether the Shock was an unnecessary misstep by the Nixon administration, or an imperfect inevitability is decidedly beyond the scope of this demonstration, which is intended only to be a showcase for informative purposes.

2 Setting up the R environment


This section briefly goes over the technical aspects of setting up R for creating the visualisations. The content here is chiefly for fellow R learners and enthusiasts. If you’re more interested in the graphs, feel free to skip this section. Otherwise click here.

2.1 Packages

For the graphs in this demonstration, I will be using the tidyverse suite of packages for RStudio as the main toolset for data wrangling, cleaning, and visualisation through ggplot2. I will also be using lubridate for datetime manipulation, stringr for string manipulation, fredr for direct data fetching through the Federal Reserve Economic Data site provided by the Federal Reserve’s API. For aesthetic customisation of the document, I use patchwork and extrafont.

library(tidyverse)   # RStudio's suite of extended tools.
library(lubridate)   # For datetime manipulation
library(extrafont)   # For custom fonts.
library(fredr)       # For direct data fetching through FRED's API.
library(patchwork)   # For plot tiling.
library(kableExtra)  # For tables

2.2 API Key

Should you the reader wish to replicate the plots in this demonstration, keep in mind that the fredr package connects through FRED’s API, and thus requires an API key to be set. Here is a sample for the syntax to set an API key, but for your personal use you will need to sign up for a free account with FRED, and set your own API key.

fredr_set_key("xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx")

2.3 Colour palette

I will also set a custom colour scale for use throughout different visualisations, for the sake of design continuity. The palettes are defined here, and displayed below. The colours and general design are inspired by vintage book covers by Pelican Books (and others in the same vein) from the 1970s.

palette_prim  <- c("#ECA400", 
                   "#006992", 
                   "#b33951")
palette_2nd   <- c("#FFD470", 
                   "#99E2FF",
                   "#DD92A1")
palette_tert  <- c("#FBF6EF",
                   "#0B0A07")
palette_grid  <- c("#768749")

scales::show_col(palette_prim %>% 
                   append(palette_2nd) %>% 
                   append(palette_grid) %>% 
                   append(palette_tert),
                 border = NA)

2.4 Custom ggplot2 theme

To avoid repetition in constructing the plots, I predefine a custom theme based on ggplot2’s theme_minimal() here.

2.4.1 Theme Code

theme_pelican70 <- function () { 
  theme_minimal(base_size=12.5, base_family="Montserrat Light") %+replace%
    theme(
      panel.background      = element_rect(fill = palette_tert[1], colour=NA),
      plot.background       = element_rect(fill = palette_tert[1], colour=NA),
      
      legend.background     = element_rect(fill = "transparent", colour=NA),
      legend.key            = element_rect(fill = "transparent", colour=NA),
      
      plot.margin           = unit(c(t=1,r=1,b=1,l=1),"cm"),
      
      plot.title            = element_text(family = "Newslab Bold",
                                           color = palette_tert[2],
                                           margin = margin(t = 2,
                                                           b = 8,
                                                           unit = "pt")),
      plot.subtitle         = element_text(family = "Newslab",
                                           color = palette_tert[2],
                                           margin = margin(t = 2,
                                                           b = 12,
                                                           unit = "pt")),
      axis.text.y           = element_text(angle = 90,
                                           color=palette_tert[2]),
      axis.text.x           = element_text(color=palette_tert[2]),
      axis.title.x          = element_text(color=palette_tert[2],
                                           face="bold.italic",
                                           margin = margin(t = 2,
                                                           b = 2,
                                                           unit = "pt")),
      axis.title.y          = element_text(color=palette_tert[2],
                                           angle = 90,
                                           face="bold.italic",
                                           margin = margin(t = 2,
                                                           b = 2,
                                                           unit = "pt")),
      strip.text.x          = element_blank(),
      
      panel.grid.major.x    = element_line(linetype = "dotted",
                                           colour = palette_grid),
      panel.grid.major.y    = element_line(linetype = "dotted",
                                           colour = palette_grid),
      panel.grid.minor.x    = element_blank(),
      panel.grid.minor.y    = element_blank(),
      
      plot.caption          = element_text(color=palette_tert[2],
                                           face="italic",
                                           size=7,
                                           hjust = 1,
                                           vjust = 1),
      plot.caption.position = "plot"
    )
}

2.4.2 Plot Example

ggplot(data=mtcars,
       aes(as.factor(gear),mpg,fill=as.factor(gear)))+
  geom_boxplot(col=palette_tert[2])+
  geom_jitter()+
  scale_fill_manual(values = palette_prim)+
  theme_pelican70()+
  labs(title="Example with mtcars dataset: Mileage by gear number",
       x = "Gears",y="Miles per Gallon", fill = "Gears")

3 Domestic Decoupling of the US Economy


This section focuses chiefly on how most of the economic growth post-71 in the US has been decoupled from the realities of the average American worker.

The shared prosperity following the end of WW2 would have one believe in “a rising tide lifts all boats” - but this seemed to have stopped being true after 1971, especially for the middle class and working class in the US. In this section, we examine the various ways in which the common American worker has not shared in the continued economic growth since what I call the great decoupling that took place around 1971.

3.1 Productivity vs. Wages

This first plot is probably the most well known: as much as labour productivity has continued to rise, since the 70s, wages have stagnated when adjusted for inflation. Using data from the Economic Policy Institute, here I recreate the plot of relative percentage rise in productivity for all workers and of hourly wage across the US.

Here, we can see clearly that in the few years after 1971, the general rate of increase for wages fell precipitously while productivity continues its previous trajectory. The growing economy had left the worker behind.

3.1.1 Plot

3.1.2 Code

prod_wage <- read_csv("data/productivity_wage.csv") %>%
  mutate_all(funs(str_replace(., "%", ""))) %>%
  mutate_all(funs(str_squish(.))) %>%
  transmute(
    year = as.numeric(Year),
    wage48 = (as.numeric(`Hourly compensation`)+100)/100,
    prod48 = (as.numeric(`Net productivity`)+100)/100
  )

w71 <- prod_wage[prod_wage$year==1971,]$wage48
p71 <- prod_wage[prod_wage$year==1971,]$prod48

prod_wage71 <- prod_wage %>% 
  transmute(
    year = year,
    wage71 = as.numeric(format(round(wage48/w71, 4), nsmall = 4)),
    prod71 = as.numeric(format(round(prod48/p71, 4), nsmall = 4))
  ) %>% 
  filter(year > 1971)

plot_prod_wage <- prod_wage %>%
  pivot_longer(cols = -year,
               names_to = "var",
               values_to = "y") %>%
  mutate(y = (y)) %>%
  ggplot(aes(
    x = year,
    y = y,
    col = var,
    group = var,
    text = paste(sep = "",
                 "Year: ",year,
                 "\n% of 1948: ",y*100,"%")
  )) +
  geom_line(size = 1.25) +
  
  theme_pelican70() +
  scale_color_manual(values = palette_prim) +
  
  scale_x_continuous(breaks = seq(1948, 2018, 8)) +
  scale_y_continuous(labels = scales::percent,
                     breaks = seq(1, 3, 1)) +
  xlim(1945, 2026) +
  
  annotate(
    "text",
    x = max(prod_wage$year),
    y = (prod_wage$wage48 %>% last()) - .5,
    label = "Hourly Wage \nsince '71",
    fontface = 1,
    col = palette_prim[2]
  ) +
  annotate(
    "text",
    x = max(prod_wage$year),
    y = (prod_wage$prod48 %>% last()) - .5,
    label = "Productivity \nsince '71",
    fontface = 1,
    col = palette_prim[1]
  ) +
  
  annotate(
    "point",
    size = 3,
    x = max(prod_wage$year),
    y = (prod_wage$prod48 %>% last()),
    col = palette_prim[1]
  ) +
  annotate(
    "point",
    size = 3,
    x = max(prod_wage$year),
    y = (prod_wage$wage48 %>% last()),
    col = palette_prim[2]
  ) +
  
  annotate(
    "text",
    x = max(prod_wage$year),
    y = (prod_wage$prod48 %>% last()) - .20,
    label = paste("+",
                  ((
                    prod_wage71$prod71 %>% last()
                  ) - 1) * 100,
                  "%",
                  sep = ""),
    fontface = 2,
    size = 5,
    col = palette_prim[1]
  ) +
  annotate(
    "text",
    x = max(prod_wage$year),
    y = (prod_wage$wage48 %>% last()) - .20,
    label = paste("+",
                  ((
                    prod_wage71$wage71 %>% last()
                  ) - 1) * 100,
                  "%",
                  sep = ""),
    fontface = 2,
    size = 5,
    col = palette_prim[2]
  ) +
  
  annotate(
    "segment",
    x = 1971,
    xend = 1971,
    y = .95,
    yend = 3.35,
    linetype = "dashed",
    size = 0.75,
    col = palette_grid
  ) +
  annotate(
    "text",
    x = 1971,
    y = 3.50,
    label = "1971",
    fontface = 2,
    col = palette_grid
  ) +
  
  labs(
    title = "Figure 1: Productivity and hourly wage rise from 1948-2018",
    subtitle = "",
    x = "Year",
    y = "% of 1948 value\n",
    caption = "Data Source: Economic Policy Institute, BLS"
  ) +
  
  theme(legend.position = "none")

ggsave(plot = plot_prod_wage,
       filename = "plots/plot_prod_wage.png",
       h = 5,
       w = 10,
       type = "cairo-png")

3.1.3 Data

Year Hourly Wage Labour Productivity
1948 1.0000 1.0000
1949 1.0624 1.0155
1950 1.1046 1.0934
1951 1.1174 1.1224
1952 1.1502 1.1549
1953 1.2082 1.1941
1954 1.2348 1.2144
1955 1.2870 1.2638
1956 1.3389 1.2659
1957 1.3708 1.3004
1958 1.3808 1.3272
1959 1.4246 1.3763
1960 1.4538 1.4006
1961 1.4784 1.4437
1962 1.5232 1.4980
1963 1.5486 1.5503
1964 1.5832 1.5994
1965 1.6227 1.6492
1966 1.6470 1.6995
1967 1.6668 1.7198
1968 1.7105 1.7713
1969 1.7439 1.7785
1970 1.7681 1.8035
1971 1.8166 1.8710
1972 1.9134 1.9220
1973 1.9096 1.9696
1974 1.8705 1.9383
1975 1.8686 1.9811
1976 1.8935 2.0359
1977 1.9282 2.0605
1978 1.9566 2.0827
1979 1.9325 2.0811
1980 1.8805 2.0677
1981 1.8736 2.1050
1982 1.8770 2.0837
1983 1.8849 2.1451
1984 1.8703 2.2021
1985 1.8618 2.2365
1986 1.8725 2.2828
1987 1.8467 2.2880
1988 1.8402 2.3201
1989 1.8393 2.3412
1990 1.8237 2.3695
1991 1.8202 2.3850
1992 1.8320 2.4748
1993 1.8346 2.4851
1994 1.8389 2.5054
1995 1.8276 2.5159
1996 1.8287 2.5624
1997 1.8487 2.6072
1998 1.8927 2.6621
1999 1.9198 2.7346
2000 1.9296 2.7947
2001 1.9560 2.8371
2002 1.9949 2.9150
2003 2.0158 3.0122
2004 2.0056 3.0929
2005 1.9973 3.1529
2006 1.9988 3.1761
2007 2.0145 3.1978
2008 2.0139 3.2139
2009 2.0930 3.2875
2010 2.1100 3.3823
2011 2.0847 3.3821
2012 2.0650 3.3957
2013 2.0840 3.4096
2014 2.0908 3.4291
2015 2.1241 3.4575
2016 2.1439 3.4634
2017 2.1467 3.4978
2018 2.1562 3.5290

3.2 GDP per Capita vs. Federal Minimum Wage

To further demonstrate that prosperity in the economy at large has not trickled down to the working class, this plot shows that while GDP per capita has risen greatly since ’71, the inflation-adjusted value of the federal minimum wage level has actually fallen sine 1971.

3.2.1 Plot

3.2.2 Code

minwage_gdpcapita <- fredr(
  series_id = "FEDMINNFRWG",
  observation_start = as.Date("1960-01-01"),
  observation_end = as.Date("2019-01-02")
) %>% rbind(
  fredr(
    series_id = "USACPIALLMINMEI",
    observation_start = as.Date("1960-01-01"),
    observation_end = as.Date("2019-01-02")
  )
) %>% 
  pivot_wider(
    id_cols = date,
    names_from = series_id,
    values_from = value
  ) %>% 
  mutate(
    adj_minwage = FEDMINNFRWG/USACPIALLMINMEI*100
  ) %>% 
  filter(month(date) == 1) %>% 
  select(date,adj_minwage) %>% 
  pivot_longer(
    cols = -date,
    names_to = "series_id",
    values_to = "value"
  ) %>% 
  rbind(
    fredr(
      series_id = "A939RX0Q048SBEA",
      observation_start = as.Date("1960-01-01"),
      observation_end = as.Date("2019-01-02")
    )
  ) %>% 
  filter(month(date) == 1) %>% 
  mutate(date = year(date)) %>% 
  group_by(series_id) %>% 
  mutate(
    val_adj = value/first(value)
  ) 

last_val_gdp <- last(minwage_gdpcapita[minwage_gdpcapita$series_id == "A939RX0Q048SBEA",]$val_adj)
last_val_fmw <- last(minwage_gdpcapita[minwage_gdpcapita$series_id == "adj_minwage",]$val_adj)

text_gdp <- paste("+",
                  as.numeric(format(round((last_val_gdp-1)*100, 2), nsmall = 2)),
                  "%",
                  sep = "")
text_fmw <- paste("-",
                  as.numeric(format(round((1-last_val_fmw)*100, 2), nsmall = 2)),
                  "%",
                  sep = "")

plot_minwage_gdppc <- minwage_gdpcapita %>% 
  ggplot(
    aes(
      x = date,
      y = val_adj,
      col = series_id,
      text = paste(sep = "",
                   "Year: ",date,
                   "% of 1960: ",val_adj)
    )
  ) + 
  geom_line(size=1.25)+
  geom_vline(
    xintercept = 1971,
    linetype = "dashed",
    size = 0.75,
    col = palette_grid
  )+
    
  xlim(1960,2032)+
  ylim(1,3.6)+
  
  scale_color_manual(values=palette_prim)+
  scale_y_continuous(labels = scales::percent,
                     breaks = seq(1, 3, 1))+
  theme_pelican70()+
  labs(
    title = "Real GDP per capita vs. Federal minimum wage rise 1960-2019",
    subtitle = "",
    x = "Year",
    y = "% of 1960 value \n",
    caption = "Data Source: FRED"
  ) +

  annotate("point",
           x = 2019,
           y = last_val_gdp,
           col = palette_prim[1],
           size=3)+
  annotate("point",
           x = 2019,
           y = last_val_fmw,
           col = palette_prim[2],
           size=3)+
  
  annotate("text",
           x = 2024,
           y = last_val_gdp+.1,
           label = text_gdp,
           col = palette_prim[1],
           fontface = 2,
           size = 5
  )+
  annotate("text",
           x = 2024,
           y = last_val_fmw+.5,
           label = text_fmw,
           col = palette_prim[2],
           fontface = 2,
           size = 5
  )+
  
  annotate("text",
           x = 2024,
           y = last_val_gdp-.1,
           label = "\n GDP/capita \nsince '71",
           col = palette_prim[1],
           size = 4
  )+
  annotate("text",
           x = 2024,
           y = last_val_fmw+.2,
           label = "\n Adj. Federal \nminimum wage \nsince '71",
           col = palette_prim[2],
           size = 4
  )+
  annotate(
    "text",
    x = 1969,
    y = 3.25,
    label = "1971",
    fontface = 2,
    col = palette_grid
  ) +
  
  theme(legend.position = "none")

ggsave(plot = plot_minwage_gdppc,
       filename = "plots/plot_minwage_gdppc.png",
       h = 5,
       w = 10,
       type = "cairo-png")

3.2.3 Data

Year Adj. Federal Min. Wage Adj. GDP per capita
1960 8.089317 18268
1961 7.953591 17816
1962 9.085652 18863
1963 8.966104 19257
1964 9.588066 20169
1965 9.495873 20997
1966 9.316706 22510
1967 9.005205 22911
1968 9.730903 23551
1969 10.652449 24365
1970 10.032466 24189
1971 9.528322 24520
1972 9.226939 25083
1973 8.902047 26718
1974 8.137923 26643
1975 9.553468 25789
1976 9.804660 27101
1977 9.318617 27706
1978 10.049521 28549
1979 10.063679 30077
1980 9.444122 30154
1981 9.126517 30316
1982 8.420010 29365
1983 8.118680 29511
1984 7.792021 31762
1985 7.526132 32920
1986 7.244589 33972
1987 7.140350 34585
1988 6.862636 35728
1989 6.556622 36929
1990 6.232394 37593
1991 6.691416 36746
1992 7.294151 37304
1993 7.063971 38029
1994 6.890029 38852
1995 6.702077 39729
1996 6.524108 40292
1997 7.076246 41532
1998 7.553450 43035
1999 7.429322 44600
2000 7.231265 45944
2001 6.971088 46531
2002 6.892363 46690
2003 6.717873 47078
2004 6.590916 48663
2005 6.400826 50081
2006 6.155510 51277
2007 6.030341 51540
2008 6.568834 51637
2009 7.352654 49491
2010 7.930209 49903
2011 7.802878 50495
2012 7.581114 51468
2013 7.462104 51921
2014 7.346113 52293
2015 7.352682 54071
2016 7.253091 54640
2017 7.076183 55401
2018 6.932642 56785
2019 6.826744 57789

3.3 Corporate Profits vs. Median Household Income

An even more dramatic rise can be seen in corporate profits since: aggregate corporate profits after tax has skyrocketed sixfold, but median household income has only doubled in the same time period. Both time series here are adjusted for inflation with a CPI deflator (2018 dollars). Granted, the trajectory for corporate profits is a lot more choppy (especially around the 2008 Global Financial Crisis), even in that era corporate profit still outstrips median household income by more than twice as much.

3.3.1 Plot

3.3.2 Code

corp_medincome <- fredr(
  series_id = "CP",
  observation_start = as.Date("1960-01-01"),
  observation_end = as.Date("2019-01-02")
) %>% rbind(
  fredr(
    series_id = "USACPIALLMINMEI",
    observation_start = as.Date("1960-01-01"),
    observation_end = as.Date("2019-01-02")
  )
) %>% 
  pivot_wider(
    id_cols = date,
    names_from = series_id,
    values_from = value
  ) %>% 
  mutate(
    adj = CP/USACPIALLMINMEI*100
  ) %>% 
  filter(month(date) == 1) %>% 
  select(date,adj) %>% 
  pivot_longer(
    cols = -date,
    names_to = "series_id",
    values_to = "value"
  ) %>% 
  rbind(
    fredr(
      series_id = "MEFAINUSA672N",
      observation_start = as.Date("1960-01-01"),
      observation_end = as.Date("2019-01-02")
    )
  ) %>% 
  filter(month(date) == 1) %>% 
  mutate(date = year(date)) %>% 
  group_by(series_id) %>% 
  mutate(
    val_adj = value/first(value)
  )


last_val_crp <- last(corp_medincome[corp_medincome$series_id == "adj",]$val_adj)
last_val_mhi <- last(corp_medincome[corp_medincome$series_id == "MEFAINUSA672N",]$val_adj)

text_crp <- paste("+",
                  as.numeric(format(round((last_val_crp-1)*100, 2), nsmall = 2)),
                  "%",
                  sep = "")
text_mhi <- paste("+",
                  as.numeric(format(round((last_val_mhi-1)*100, 2), nsmall = 2)),
                  "%",
                  sep = "")

plot_corp_medincome <- corp_medincome %>% 
  ggplot(
    aes(
      x = date,
      y = val_adj,
      col = series_id
    )
  ) + 
  geom_line(size=1.25)+
  geom_vline(
    xintercept = 1971,
    linetype = "dashed",
    size = 0.75,
    col = palette_grid
  )+
    
  xlim(1960,2032)+
  ylim(1,3.6)+
  
  scale_color_manual(values=palette_prim)+
  scale_y_continuous(labels = scales::percent)+
  theme_pelican70()+
  labs(
    title = "Adjusted corporate profits vs. median household income 1960-2019",
    subtitle = "",
    x = "Year",
    y = "% of 1960 value \n",
    caption = "Data Source: FRED"
  ) +

  annotate("point",
           x = 2019,
           y = last_val_crp,
           col = palette_prim[1],
           size=3)+
  annotate("point",
           x = 2019,
           y = last_val_mhi,
           col = palette_prim[2],
           size=3)+
  
  annotate("text",
           x = 2025,
           y = last_val_crp+.2,
           label = text_crp,
           col = palette_prim[1],
           fontface = 2,
           size = 5
  )+
  annotate("text",
           x = 2024,
           y = last_val_mhi+.39,
           label = text_mhi,
           col = palette_prim[2],
           fontface = 2,
           size = 5
  )+
  
  annotate("text",
           x = 2025,
           y = last_val_crp-.2,
           label = "\n Corporate Profits \nsince '71",
           col = palette_prim[1],
           size = 4
  )+
  annotate("text",
           x = 2024,
           y = last_val_mhi-.21,
           label = "\n Median \nHousehold income \nsince '71",
           col = palette_prim[2],
           size = 4
  )+
  annotate(
    "text",
    x = 1969,
    y = 7,
    label = "1971",
    fontface = 2,
    col = palette_grid
  ) +
  
  theme(legend.position = "none")

ggsave(plot = plot_corp_medincome,
       filename = "plots/plot_corp_medincome.png",
       h = 5,
       w = 10,
       type = "cairo-png")

3.3.3 Data

Year Corporate Profits After Tax (billions) Real Median Family Income
1960 280.0036 42574
1961 232.4278 43012
1962 278.0683 44229
1963 286.1980 45773
1964 340.6372 47471
1965 385.1450 49514
1966 433.1747 52129
1967 397.3241 53240
1968 408.8369 55745
1969 412.7425 58317
1970 346.8913 58137
1971 358.2173 58056
1972 419.7392 60920
1973 531.6024 62153
1974 572.9911 60493
1975 430.8478 59438
1976 565.6948 61281
1977 607.0512 61693
1978 652.0395 63616
1979 737.4178 64519
1980 680.1108 62275
1981 580.7461 60597
1982 461.1350 59814
1983 426.9457 60171
1984 511.9660 62122
1985 460.0331 63019
1986 382.4278 65783
1987 400.3520 66859
1988 511.3975 67072
1989 538.4259 68299
1990 500.2082 67258
1991 534.0683 65973
1992 563.9426 65508
1993 546.7630 64571
1994 656.5711 66370
1995 737.3910 67865
1996 805.8547 68854
1997 814.5340 71011
1998 754.7715 73472
1999 754.5291 75163
2000 740.7820 75526
2001 715.8374 74413
2002 720.1355 73647
2003 941.1858 73405
2004 1187.9377 73348
2005 1527.0718 73744
2006 1658.8931 74241
2007 1552.5998 75838
2008 1415.6915 73230
2009 1215.3858 71774
2010 1674.2083 70783
2011 1575.8833 69461
2012 1965.8843 69433
2013 1818.0484 71970
2014 1791.0288 72027
2015 1796.0716 76290
2016 1694.5861 77459
2017 1853.5374 79404
2018 1817.0092 80071
2019 1783.7735 86011

3.4 Wage Labour Hours to buy into Stock Market Indices

Without government pension programmes, US workers often rely on retirement accounts to prepare for the future. A large amount of this capital stored in retirement accounts (401k, etc.), a large portion of which is distributed into major market indices, such as the Dow Jones Industrial Average (^DJI), the S&P 500 (^GSPC), or the Russell 2000 (^RUT). But when corporate profitability is high compared to hourly wage, the average worker might have had a more difficult time hitching themselves to the profit ride that the stock market has been riding on. Here, I decided to base my analysis on how many hours it would take for a worker to buy the value of the S&P 500 - mostly because the S&P 500 and its derivative products have had a history of being a popular investment.

Admittedly the trend reversal here appears to be delayed - at least a couple of years after 1971 itself. Still, I believe this trend still fits with the overall theme: since the 70s, the average worker has had to work nearly ten times the amount of hours to buy into the most popular investment instrument.

3.4.1 Plot

3.4.2 Code

Here I call two extra packages called tidyquant and quantmod to fetch and manipulate stock market data.

library(tidyquant)
library(quantmod)

sp500 <- read_csv("data/avg_hourly_earning.csv") %>% 
  pivot_longer(cols = -Year,
               names_to = "month",
               values_to ="value") %>% 
  mutate(rtime = paste("01",month,Year,sep=" "),
         date = dmy(rtime),
         series_id = "avg_hourly_wage") %>% 
  select(date,series_id,value) %>% 
  rbind(
  fredr(
    series_id = "USACPIALLMINMEI",
    observation_start = as.Date("1960-01-01"),
    observation_end = as.Date("2019-01-02")
  )
) %>% 
  mutate(series_id = ifelse(series_id == "USACPIALLMINMEI", "cpi",series_id)) %>% 
  pivot_wider(
    id_cols = date,
    names_from = series_id,
    values_from = value
  ) %>% 
  mutate(wage = avg_hourly_wage * cpi / 100) %>% 
  select(date,wage) %>% 
  filter(!is.na(wage)) %>% 
  left_join(getSymbols("^GSPC",
           from = "1964-01-01",
           to = "2019-01-01",
           src = "yahoo",
           auto.assign=F) %>% 
  as.data.frame() %>% 
  rownames_to_column("date") %>% 
  select(date,sp500 = GSPC.Close) %>% 
  mutate(date = as.Date(date),
         year = year(date),
         month = month(date)) %>% 
  group_by(year,month) %>% 
  summarise(sp500 = first(sp500)) %>% 
  ungroup() %>% 
  mutate(date = paste(year,month,"01",sep="-") %>% ymd(),
            sp500 = sp500) %>% 
  select(date,sp500)) %>% 
  mutate(hour_to_buy = sp500/wage) 

plot_sp500 <- sp500 %>% 
  ggplot(
    aes(
      x = date,
      y = hour_to_buy
    )
  )+
  geom_smooth(col   = palette_prim[2],
              fill  = palette_2nd[2],
              size  = 1.6,
              alpha = .75)+
  geom_line(col  = palette_prim[3],
            size = 1.25)+
  geom_vline(
    xintercept = 1971,
    linetype = "dashed",
    size = 0.75,
    col = palette_grid
  )+
  theme_pelican70()+
  labs(
    title = "Hours to buy the S&P 500 1964-2019",
    subtitle = "(with Loess smoothed trendline)",
    x = "Year",
    y = "Hours\n",
    caption = "Data Source: Yahoo Finance, BLS”"
  ) + 
  theme(legend.position = "none")+
  annotate(
    "text",
    x = as.Date("1972-12-01"),
    y = 300,
    label = "1971",
    fontface = 2,
    col = palette_grid
  )

ggsave(plot = plot_sp500,
       filename = "plots/plot_sp500.png",
       h = 5,
       w = 10,
       type = "cairo-png")

3.4.3 Data

Year & Month Avg. Nominal Hourly Wage for Nonmanagement Workers S&P500 Nominal Value Labour Hours to Buy S&P500
1964-1 1.048178 75.43 71.96297
1964-2 1.048178 76.97 73.43219
1964-3 1.048178 77.97 74.38622
1964-4 1.056000 79.24 75.03786
1964-5 1.056000 80.17 75.91854
1964-6 1.060726 80.11 75.52377
1964-7 1.065459 82.27 77.21551
1964-8 1.064649 83.00 77.95994
1964-9 1.073332 82.18 76.56529
1964-10 1.069396 84.08 78.62383
1964-11 1.076783 85.18 79.10597
1964-12 1.082049 83.55 77.21462
1965-1 1.082049 84.23 77.84306
1965-2 1.085998 87.58 80.64471
1965-3 1.089479 87.25 80.08416
1965-4 1.090310 86.32 79.17015
1965-5 1.098259 89.23 81.24679
1965-6 1.102588 88.72 80.46526
1965-7 1.102588 84.48 76.61977
1965-8 1.102588 85.42 77.47230
1965-9 1.110587 87.17 78.49002
1965-10 1.119451 89.90 80.30720
1965-11 1.119451 92.23 82.38858
1965-12 1.118958 91.50 81.77253
1966-1 1.128349 92.18 81.69455
1966-2 1.127345 92.16 81.74958
1966-3 1.128160 90.06 79.82913
1966-4 1.136551 89.94 79.13413
1966-5 1.140640 90.90 79.69213
1966-6 1.144171 86.10 75.25098
1966-7 1.149074 85.61 74.50349
1966-8 1.145108 82.31 71.87970
1966-9 1.161663 77.70 66.88684
1966-10 1.165992 74.90 64.23713
1966-11 1.165992 80.81 69.30577
1966-12 1.165992 80.08 68.67970
1967-1 1.174321 80.38 68.44807
1967-2 1.178485 86.43 73.33992
1967-3 1.177890 87.68 74.43818
1967-4 1.182856 89.24 75.44451
1967-5 1.186430 93.84 79.09445
1967-6 1.195623 90.23 75.46693
1967-7 1.199214 90.91 75.80802
1967-8 1.199977 95.37 79.47651
1967-9 1.212065 93.68 77.28959
1967-10 1.215672 96.32 79.23188
1967-11 1.220706 92.71 75.94787
1967-12 1.220026 94.50 77.45734
1968-1 1.241611 96.11 77.40748
1968-2 1.245253 92.56 74.33031
1968-3 1.241658 89.11 71.76696
1968-4 1.249632 92.48 74.00580
1968-5 1.259087 97.97 77.81036
1968-6 1.261994 99.99 79.23176
1968-7 1.270740 99.40 78.22214
1968-8 1.271428 97.28 76.51241
1968-9 1.292831 99.32 76.82363
1968-10 1.295730 102.86 79.38383
1968-11 1.300894 103.06 79.22244
1968-12 1.304569 108.12 82.87796
1969-1 1.309746 103.93 79.35128
1969-2 1.317104 102.89 78.11837
1969-3 1.322049 98.38 74.41481
1969-4 1.329373 101.42 76.29160
1969-5 1.339178 103.51 77.29367
1969-6 1.346536 102.94 76.44799
1969-7 1.350789 98.08 72.60941
1969-8 1.355008 93.47 68.98113
1969-9 1.375889 95.54 69.43876
1969-10 1.380158 92.52 67.03578
1969-11 1.384394 97.15 70.17509
1969-12 1.385415 93.22 67.28668
1970-1 1.393875 93.00 66.72049
1970-2 1.398043 85.75 61.33573
1970-3 1.405401 89.71 63.83230
1970-4 1.409941 90.07 63.88210
1970-5 1.418489 81.44 57.41321
1970-6 1.422565 77.84 54.71807
1970-7 1.431543 72.94 50.95202
1970-8 1.439770 77.02 53.49465
1970-9 1.457077 80.95 55.55643
1970-10 1.452875 84.32 58.03666
1970-11 1.456908 83.51 57.32002
1970-12 1.469304 87.47 59.53159
1971-1 1.482738 91.15 61.47413
1971-2 1.489830 96.42 64.71880
1971-3 1.490188 97.00 65.09244
1971-4 1.498989 100.39 66.97178
1971-5 1.511567 103.29 68.33308
1971-6 1.519393 100.20 65.94738
1971-7 1.524853 99.78 65.43583
1971-8 1.532042 95.96 62.63536
1971-9 1.549256 99.07 63.94683
1971-10 1.549602 98.93 63.84220
1971-11 1.549602 92.80 59.88635
1971-12 1.565850 95.44 60.95093
1972-1 1.595329 101.67 63.72982
1972-2 1.599607 104.01 65.02223
1972-3 1.612213 107.35 66.58547
1972-4 1.621360 107.48 66.29001
1972-5 1.625267 106.69 65.64458
1972-6 1.625655 109.69 67.47432
1972-7 1.633452 107.49 65.80540
1972-8 1.642667 108.40 65.99025
1972-9 1.662564 111.51 67.07109
1972-10 1.675817 110.16 65.73512
1972-11 1.676200 112.67 67.21750
1972-12 1.680154 117.38 69.86265
1973-1 1.687702 119.10 70.56934
1973-2 1.692347 114.76 67.81115
1973-3 1.697165 111.05 65.43264
1973-4 1.708924 110.18 64.47332
1973-5 1.722535 107.10 62.17582
1973-6 1.730576 103.93 60.05514
1973-7 1.743837 102.90 59.00781
1973-8 1.739175 106.83 61.42568
1973-9 1.773544 104.51 58.92722
1973-10 1.773847 108.21 61.00299
1973-11 1.777771 107.69 60.57585
1973-12 1.785492 93.90 52.59054
1974-1 1.789154 97.68 54.59562
1974-2 1.798251 95.32 53.00707
1974-3 1.811026 95.53 52.74910
1974-4 1.814553 93.25 51.39006
1974-5 1.845437 92.22 49.97190
1974-6 1.858559 89.10 47.94037
1974-7 1.861225 86.02 46.21687
1974-8 1.875393 78.75 41.99120
1974-9 1.912842 70.52 36.86661
1974-10 1.920964 63.39 32.99907
1974-11 1.920791 73.88 38.46333
1974-12 1.929140 68.11 35.30589
1975-1 1.938772 70.23 36.22396
1975-2 1.942582 77.82 40.06008
1975-3 1.949982 83.03 42.57987
1975-4 1.955151 82.64 42.26784
1975-5 1.963994 88.10 44.85757
1975-6 1.976500 92.58 46.84039
1975-7 1.984904 94.85 47.78568
1975-8 1.988566 87.99 44.24796
1975-9 2.022589 85.48 42.26266
1975-10 2.026753 82.93 40.91766
1975-11 2.039187 88.09 43.19859
1975-12 2.044221 90.67 44.35431
1976-1 2.057287 90.90 44.18440
1976-2 2.069396 100.87 48.74370
1976-3 2.073104 100.02 48.24648
1976-4 2.082889 102.24 49.08568
1976-5 2.102507 100.92 47.99983
1976-6 2.106482 99.85 47.40131
1976-7 2.120017 103.59 48.86282
1976-8 2.131155 103.19 48.41975
1976-9 2.170173 104.06 47.95008
1976-10 2.174148 104.17 47.91302
1976-11 2.187691 103.10 47.12731
1976-12 2.195235 102.49 46.68748
1977-1 2.209019 107.00 48.43780
1977-2 2.216714 102.54 46.25765
1977-3 2.229207 100.66 45.15507
1977-4 2.245409 99.21 44.18350
1977-5 2.261724 98.93 43.74097
1977-6 2.271605 96.93 42.67027
1977-7 2.282832 100.10 43.84904
1977-8 2.285153 99.12 43.37566
1977-9 2.321116 96.83 41.71700
1977-10 2.341672 96.74 41.31237
1977-11 2.347853 91.35 38.90789
1977-12 2.347578 94.69 40.33518
1978-1 2.381158 93.82 39.40099
1978-2 2.393744 89.93 37.56876
1978-3 2.402072 87.19 36.29782
1978-4 2.423712 88.46 36.49773
1978-5 2.435585 97.67 40.10125
1978-6 2.448263 97.35 39.76288
1978-7 2.469810 95.09 38.50093
1978-8 2.469949 100.66 40.75387
1978-9 2.519524 103.68 41.15063
1978-10 2.536594 102.96 40.58985
1978-11 2.545092 96.85 38.05364
1978-12 2.553564 96.28 37.70417
1979-1 2.579077 96.73 37.50567
1979-2 2.597624 99.96 38.48132
1979-3 2.600379 96.90 37.26380
1979-4 2.600396 100.90 38.80179
1979-5 2.627512 101.68 38.69821
1979-6 2.635558 99.17 37.62771
1979-7 2.652384 101.99 38.45221
1979-8 2.655987 104.17 39.22083
1979-9 2.713105 107.44 39.60039
1979-10 2.715890 108.56 39.97217
1979-11 2.731564 102.57 37.54992
1979-12 2.750647 105.83 38.47459
1980-1 2.767118 105.76 38.22027
1980-2 2.782940 115.12 41.36633
1980-3 2.808368 112.50 40.05885
1980-4 2.822836 102.18 36.19765
1980-5 2.830008 105.46 37.26491
1980-6 2.847188 110.76 38.90154
1980-7 2.861145 114.93 40.16923
1980-8 2.878388 121.21 42.11037
1980-9 2.920297 123.74 42.37240
1980-10 2.948109 127.13 43.12255
1980-11 2.976052 129.04 43.35945
1980-12 2.982052 137.21 46.01194
1981-1 3.024593 136.34 45.07714
1981-2 3.037339 126.91 41.78328
1981-3 3.054338 132.01 43.22050
1981-4 3.071286 136.57 44.46672
1981-5 3.087838 132.72 42.98154
1981-6 3.100056 132.41 42.71213
1981-7 3.114950 129.77 41.66039
1981-8 3.134860 130.48 41.62228
1981-9 3.177224 123.02 38.71934
1981-10 3.187982 117.08 36.72542
1981-11 3.214035 124.20 38.64301
1981-12 3.204496 126.10 39.35096
1982-1 3.254509 122.74 37.71383
1982-2 3.252889 117.78 36.20781
1982-3 3.253437 113.31 34.82778
1982-4 3.267209 113.79 34.82789
1982-5 3.290110 116.82 35.50641
1982-6 3.290397 111.68 33.94119
1982-7 3.303244 108.71 32.91007
1982-8 3.305898 108.98 32.96533
1982-9 3.341579 118.25 35.38746
1982-10 3.351819 121.97 36.38920
1982-11 3.361531 135.47 40.30009
1982-12 3.364282 138.72 41.23317
1983-1 3.408313 138.34 40.58900
1983-2 3.415928 142.96 41.85100
1983-3 3.395275 150.88 44.43822
1983-4 3.415392 153.02 44.80306
1983-5 3.431990 162.11 47.23498
1983-6 3.433973 162.55 47.33584
1983-7 3.447778 168.64 48.91266
1983-8 3.424311 162.04 47.32046
1983-9 3.488134 164.23 47.08248
1983-10 3.507048 165.81 47.27908
1983-11 3.513993 163.66 46.57380
1983-12 3.521739 166.49 47.27494
1984-1 3.564095 164.04 46.02571
1984-2 3.564301 162.74 45.65832
1984-3 3.575592 158.19 44.24163
1984-4 3.601716 157.98 43.86242
1984-5 3.586021 161.68 45.08618
1984-6 3.596426 153.24 42.60897
1984-7 3.605906 153.20 42.48586
1984-8 3.575672 154.08 43.09120
1984-9 3.628221 164.88 45.44376
1984-10 3.629702 164.62 45.35359
1984-11 3.643030 167.49 45.97547
1984-12 3.656358 162.82 44.53065
1985-1 3.672205 165.37 45.03289
1985-2 3.685137 178.63 48.47310
1985-3 3.676597 183.23 49.83684
1985-4 3.684854 181.27 49.19327
1985-5 3.680534 178.37 48.46308
1985-6 3.690824 189.32 51.29478
1985-7 3.688588 192.43 52.16902
1985-8 3.690875 192.11 52.05000
1985-9 3.746820 187.91 50.15187
1985-10 3.742314 185.07 49.45336
1985-11 3.748043 191.53 51.10133
1985-12 3.772194 200.46 53.14150
1986-1 3.768675 209.59 55.61371
1986-2 3.786028 213.96 56.51305
1986-3 3.787070 225.42 59.52359
1986-4 3.784690 235.14 62.12925
1986-5 3.781362 235.16 62.18924
1986-6 3.783716 245.04 64.76173
1986-7 3.774476 252.04 66.77483
1986-8 3.776742 234.91 62.19912
1986-9 3.821852 248.52 65.02606
1986-10 3.829974 233.60 60.99258
1986-11 3.852078 245.80 63.80971
1986-12 3.846243 249.05 64.75150
1987-1 3.865917 246.45 63.74943
1987-2 3.870406 276.45 71.42662
1987-3 3.868828 283.00 73.14877
1987-4 3.870516 292.38 75.54032
1987-5 3.879481 288.03 74.24446
1987-6 3.864470 289.83 74.99865
1987-7 3.860280 302.94 78.47617
1987-8 3.875806 317.57 81.93650
1987-9 3.925246 323.40 82.38974
1987-10 3.940350 327.33 83.07129
1987-11 3.958374 255.75 64.60986
1987-12 3.958374 232.00 58.60992
1988-1 3.978428 255.94 64.33195
1988-2 3.974061 255.04 64.17617
1988-3 3.976445 267.22 67.20073
1988-4 4.001865 256.09 63.99266
1988-5 4.010577 261.56 65.21754
1988-6 3.997772 266.69 66.70965
1988-7 4.004713 271.78 67.86504
1988-8 4.001527 272.21 68.02652
1988-9 4.068864 258.35 63.49438
1988-10 4.092593 271.38 66.31005
1988-11 4.095997 279.06 68.12993
1988-12 4.097723 272.49 66.49790
1989-1 4.138564 275.31 66.52308
1989-2 4.140260 297.09 71.75636
1989-3 4.148614 287.11 69.20625
1989-4 4.160170 296.39 71.24469
1989-5 4.147264 309.12 74.53589
1989-6 4.146842 321.97 77.64222
1989-7 4.167364 319.23 76.60239
1989-8 4.158292 343.75 82.66614
1989-9 4.229654 353.73 83.63095
1989-10 4.244658 350.87 82.66156
1989-11 4.254796 341.20 80.19186
1989-12 4.266875 350.63 82.17489
1990-1 4.283988 359.69 83.96149
1990-2 4.304164 328.79 76.38882
1990-3 4.316842 332.74 77.07949
1990-4 4.334428 338.70 78.14181
1990-5 4.339064 332.25 76.57181
1990-6 4.340651 363.16 83.66488
1990-7 4.357358 359.54 82.51330
1990-8 4.353038 355.52 81.67169
1990-9 4.411819 323.09 73.23284
1990-10 4.404621 314.94 71.50218
1990-11 4.408874 307.02 69.63683
1990-12 4.425809 324.10 73.22955
1991-1 4.446592 326.45 73.41577
1991-2 4.453200 343.05 77.03450
1991-3 4.454111 370.47 83.17485
1991-4 4.477822 371.30 82.91977
1991-5 4.479628 380.29 84.89321
1991-6 4.487104 388.06 86.48339
1991-7 4.487957 377.92 84.20759
1991-8 4.489610 387.12 86.22574
1991-9 4.549851 392.15 86.18964
1991-10 4.544889 389.20 85.63465
1991-11 4.546492 391.32 86.07075
1991-12 4.567246 381.40 83.50765
1992-1 4.573870 417.26 91.22690
1992-2 4.584582 409.53 89.32765
1992-3 4.595983 412.45 89.74142
1992-4 4.596696 404.23 87.93926
1992-5 4.597392 412.53 89.73131
1992-6 4.590185 417.30 90.91136
1992-7 4.594080 412.88 89.87219
1992-8 4.601214 425.09 92.38648
1992-9 4.650046 416.07 89.47653
1992-10 4.660518 416.29 89.32269
1992-11 4.673082 422.75 90.46491
1992-12 4.669792 430.78 92.24823
1993-1 4.704861 435.38 92.53834
1993-2 4.703245 442.52 94.08823
1993-3 4.713620 442.01 93.77295
1993-4 4.720674 450.30 95.38892
1993-5 4.727231 442.46 93.59814
1993-6 4.709417 453.83 96.36648
1993-7 4.709417 449.02 95.34512
1993-8 4.722463 450.15 95.32102
1993-9 4.775100 463.15 96.99272
1993-10 4.782552 461.28 96.45061
1993-11 4.791985 469.10 97.89262
1993-12 4.804288 461.89 96.14119
1994-1 4.842142 465.44 96.12275
1994-2 4.846323 479.62 98.96575
1994-3 4.837999 464.44 95.99837
1994-4 4.850791 438.92 90.48421
1994-5 4.860305 453.02 93.20813
1994-6 4.833071 457.63 94.68721
1994-7 4.839872 446.20 92.19252
1994-8 4.834295 461.01 95.36242
1994-9 4.897699 473.17 96.61067
1994-10 4.919900 461.74 93.85149
1994-11 4.920166 468.42 95.20411
1994-12 4.932798 448.92 91.00717
1995-1 4.958910 459.11 92.58284
1995-2 4.959606 470.40 94.84624
1995-3 4.963264 485.65 97.84891
1995-4 4.979655 501.85 100.78007
1995-5 4.970226 514.26 103.46814
1995-6 4.960720 533.49 107.54285
1995-7 4.986457 547.09 109.71519
1995-8 4.980183 559.64 112.37339
1995-9 5.048127 563.84 111.69291
1995-10 5.064603 581.72 114.85994
1995-11 5.061308 584.22 115.42866
1995-12 5.070965 606.98 119.69713
1996-1 5.113726 620.73 121.38507
1996-2 5.110680 638.46 124.92663
1996-3 5.110798 644.37 126.08012
1996-4 5.137087 653.73 127.25694
1996-5 5.127126 654.58 127.66997
1996-6 5.143623 667.68 129.80734
1996-7 5.140222 675.88 131.48849
1996-8 5.156681 650.02 126.05396
1996-9 5.232992 654.72 125.11389
1996-10 5.229536 689.08 131.76694
1996-11 5.246138 703.77 134.15010
1996-12 5.272905 756.56 143.48069
1997-1 5.302953 737.01 138.98106
1997-2 5.306151 786.73 148.26754
1997-3 5.326200 795.31 149.32033
1997-4 5.332858 759.64 142.44520
1997-5 5.329529 798.53 149.83125
1997-6 5.336187 846.36 158.60764
1997-7 5.336073 891.03 166.98236
1997-8 5.366400 947.14 176.49449
1997-9 5.427358 927.58 170.90822
1997-10 5.454461 955.41 175.16120
1997-11 5.485155 938.99 171.18750
1997-12 5.491973 974.77 177.48995
1998-1 5.529460 975.04 176.33548
1998-2 5.553387 1001.27 180.29900
1998-3 5.563677 1047.70 188.31071
1998-4 5.573967 1108.15 198.80813
1998-5 5.570520 1121.00 201.23794
1998-6 5.556732 1090.98 196.33481
1998-7 5.563550 1148.56 206.44373
1998-8 5.604838 1112.44 198.47850
1998-9 5.660016 994.26 175.66382
1998-10 5.666935 986.39 174.06058
1998-11 5.694613 1111.60 195.20204
1998-12 5.691140 1175.28 206.51046
1999-1 5.739690 1228.10 213.96627
1999-2 5.739736 1273.00 221.78721
1999-3 5.750220 1236.16 214.97611
1999-4 5.778016 1293.72 223.90384
1999-5 5.792040 1354.63 233.87787
1999-6 5.763992 1294.26 224.54231
1999-7 5.781332 1380.96 238.86536
1999-8 5.795205 1328.05 229.16362
1999-9 5.865453 1331.07 226.93388
1999-10 5.875933 1282.81 218.31598
1999-11 5.879426 1354.12 230.31498
1999-12 5.900729 1397.72 236.87244
2000-1 5.946746 1455.22 244.70860
2000-2 5.946156 1409.28 237.00691
2000-3 5.951843 1379.19 231.72485
2000-4 6.005911 1505.97 250.74797
2000-5 5.983980 1468.25 245.36345
2000-6 5.979014 1448.81 242.31588
2000-7 6.014758 1469.54 244.32237
2000-8 6.014758 1438.10 239.09522
2000-9 6.090057 1520.77 249.71360
2000-10 6.122599 1436.23 234.57849
2000-11 6.126118 1421.22 231.99359
2000-12 6.151964 1315.23 213.79027
2001-1 6.176080 1283.27 207.78066
2001-2 6.193353 1373.47 221.76516
2001-3 6.207445 1241.23 199.95827
2001-4 6.239569 1145.87 183.64569
2001-5 6.222803 1266.44 203.51601
2001-6 6.218288 1260.67 202.73587
2001-7 6.245755 1236.72 198.00970
2001-8 6.245755 1215.93 194.68105
2001-9 6.319040 1132.94 179.28987
2001-10 6.320268 1038.55 164.32057
2001-11 6.347022 1084.10 170.80452
2001-12 6.381618 1129.90 177.05541
2002-1 6.396064 1154.67 180.52821
2002-2 6.398841 1122.20 175.37551
2002-3 6.397111 1131.78 176.92050
2002-4 6.410131 1146.54 178.86375
2002-5 6.402545 1086.46 169.69189
2002-6 6.428876 1040.68 161.87588
2002-7 6.413228 968.65 151.03939
2002-8 6.442217 884.66 137.32228
2002-9 6.521642 878.02 134.63174
2002-10 6.524802 847.91 129.95183
2002-11 6.540101 900.96 137.75935
2002-12 6.579098 934.53 142.04532
2003-1 6.585194 909.03 138.04149
2003-2 6.620483 860.32 129.94823
2003-3 6.605855 834.81 126.37425
2003-4 6.591510 858.48 130.24025
2003-5 6.596236 916.30 138.91256
2003-6 6.611176 967.00 146.26748
2003-7 6.610614 982.32 148.59738
2003-8 6.620200 980.15 148.05444
2003-9 6.672973 1021.99 153.15363
2003-10 6.665767 1018.22 152.75362
2003-11 6.710025 1059.02 157.82654
2003-12 6.694975 1070.12 159.83927
2004-1 6.727669 1108.48 164.76434
2004-2 6.740428 1135.26 168.42551
2004-3 6.728522 1155.97 171.80148
2004-4 6.742132 1132.17 167.92462
2004-5 6.757646 1117.49 165.36675
2004-6 6.723062 1121.20 166.76924
2004-7 6.744394 1128.94 167.38939
2004-8 6.771940 1106.62 163.41254
2004-9 6.826295 1105.91 162.00736
2004-10 6.838079 1131.50 165.47045
2004-11 6.841661 1130.51 165.23911
2004-12 6.856732 1191.37 173.75187
2005-1 6.911373 1202.08 173.92780
2005-2 6.894594 1189.41 172.51342
2005-3 6.907737 1210.41 175.22527
2005-4 6.913141 1172.92 169.66528
2005-5 6.930642 1162.16 167.68432
2005-6 6.901383 1202.22 174.19987
2005-7 6.933317 1194.44 172.27540
2005-8 6.935654 1235.35 178.11585
2005-9 6.978470 1221.59 175.05126
2005-10 7.042938 1226.70 174.17447
2005-11 7.028053 1202.76 171.13702
2005-12 7.057722 1264.67 179.18956
2006-1 7.128248 1268.80 177.99605
2006-2 7.134244 1282.46 179.76117
2006-3 7.140020 1291.24 180.84544
2006-4 7.200770 1297.81 180.23211
2006-5 7.159613 1305.19 182.29895
2006-6 7.165195 1285.71 179.43824
2006-7 7.212141 1280.19 177.50484
2006-8 7.209112 1270.92 176.29357
2006-9 7.293603 1311.01 179.74792
2006-10 7.347718 1331.32 181.18823
2006-11 7.328293 1367.81 186.64784
2006-12 7.364746 1396.71 189.64808
2007-1 7.412847 1416.60 191.10065
2007-2 7.435337 1445.94 194.46864
2007-3 7.442393 1403.17 188.53748
2007-4 7.482020 1424.55 190.39646
2007-5 7.448778 1486.30 199.53609
2007-6 7.463214 1536.34 205.85502
2007-7 7.522834 1519.43 201.97576
2007-8 7.517810 1465.81 194.97834
2007-9 7.608899 1489.42 195.74711
2007-10 7.589915 1547.04 203.82837
2007-11 7.590659 1508.44 198.72320
2007-12 7.638736 1472.42 192.75702
2008-1 7.658894 1447.16 188.95158
2008-2 7.690067 1395.42 181.45745
2008-3 7.729700 1331.34 172.23696
2008-4 7.713133 1370.18 177.64247
2008-5 7.695825 1409.34 183.13045
2008-6 7.708752 1385.67 179.75284
2008-7 7.721389 1284.91 166.40918
2008-8 7.764517 1260.31 162.31662
2008-9 7.827623 1277.58 163.21428
2008-10 7.858203 1161.06 147.75134
2008-11 7.940719 966.30 121.68922
2008-12 7.956160 816.21 102.58844
2009-1 7.990788 931.80 116.60927
2009-2 8.030526 825.44 102.78779
2009-3 8.050054 700.82 87.05780
2009-4 8.016169 811.08 101.18050
2009-5 7.985189 877.52 109.89345
2009-6 7.953678 942.87 118.54516
2009-7 7.977410 923.33 115.74308
2009-8 8.022621 1002.63 124.97537
2009-9 8.064087 998.04 123.76355
2009-10 8.080974 1029.85 127.44132
2009-11 8.123202 1042.88 128.38287
2009-12 8.099784 1108.86 136.89994
2010-1 8.154892 1132.99 138.93378
2010-2 8.166069 1089.19 133.37996
2010-3 8.135326 1115.71 137.14385
2010-4 8.158655 1178.10 144.39881
2010-5 8.183390 1202.26 146.91468
2010-6 8.120223 1070.71 131.85721
2010-7 8.140333 1027.37 126.20736
2010-8 8.188415 1125.86 137.49424
2010-9 8.220828 1080.29 131.40892
2010-10 8.258747 1146.24 138.79103
2010-11 8.262221 1184.38 143.34886
2010-12 8.248677 1206.07 146.21374
2011-1 8.362299 1271.87 152.09573
2011-2 8.310164 1307.59 157.34828
2011-3 8.278057 1306.33 157.80635
2011-4 8.293407 1332.41 160.65895
2011-5 8.303820 1361.22 163.92697
2011-6 8.247309 1314.55 159.39138
2011-7 8.292744 1339.67 161.54725
2011-8 8.277380 1286.94 155.47673
2011-9 8.347385 1204.42 144.28711
2011-10 8.416143 1099.23 130.60971
2011-11 8.370864 1218.28 145.53814
2011-12 8.378781 1244.58 148.53950
2012-1 8.463466 1277.06 150.89091
2012-2 8.414282 1324.09 157.36220
2012-3 8.400758 1374.09 163.56737
2012-4 8.464968 1419.04 167.63680
2012-5 8.396857 1405.82 167.42216
2012-6 8.384544 1278.04 152.42809
2012-7 8.448208 1365.51 161.63309
2012-8 8.388305 1375.32 163.95685
2012-9 8.494078 1404.94 165.40229
2012-10 8.481015 1444.49 170.32041
2012-11 8.499111 1427.59 167.96933
2012-12 8.553719 1409.46 164.77744
2013-1 8.598446 1462.42 170.07956
2013-2 8.600301 1513.17 175.94385
2013-3 8.573682 1518.20 177.07678
2013-4 8.604011 1562.17 181.56300
2013-5 8.560360 1582.70 184.88707
2013-6 8.590755 1640.42 190.95180
2013-7 8.584284 1614.96 188.12985
2013-8 8.574875 1706.87 199.05480
2013-9 8.703395 1639.77 188.40578
2013-10 8.680982 1695.00 195.25441
2013-11 8.712419 1761.64 202.19873
2013-12 8.751001 1800.90 205.79359
2014-1 8.793427 1831.98 208.33517
2014-2 8.865566 1741.89 196.47814
2014-3 8.832936 1845.73 208.95996
2014-4 8.812044 1885.52 213.97078
2014-5 8.772561 1883.68 214.72409
2014-6 8.798952 1924.97 218.77264
2014-7 8.795519 1973.32 224.35515
2014-8 8.790861 1925.15 218.99448
2014-9 8.857734 2002.28 226.04880
2014-10 8.895586 1946.16 218.77818
2014-11 8.967116 2017.81 225.02330
2014-12 8.906365 2053.44 230.55870
2015-1 9.032078 2058.20 227.87667
2015-2 9.061402 2020.85 223.01736
2015-3 9.065522 2117.39 233.56513
2015-4 9.034040 2059.69 227.99213
2015-5 9.019889 2108.29 233.73792
2015-6 8.981005 2111.73 235.13291
2015-7 9.011815 2077.42 230.52182
2015-8 9.059380 2098.04 231.58759
2015-9 9.095473 1913.85 210.41786
2015-10 9.141556 1923.82 210.44776
2015-11 9.192355 2104.05 228.89129
2015-12 9.160944 2102.63 229.52109
2016-1 9.266050 2012.66 217.20799
2016-2 9.273676 1939.38 209.12742
2016-3 9.263374 1978.35 213.56690
2016-4 9.297197 2072.78 222.94676
2016-5 9.304405 2081.43 223.70371
2016-6 9.233276 2099.33 227.36567
2016-7 9.289402 2102.95 226.38163
2016-8 9.277610 2170.84 233.98699
2016-9 9.361030 2170.86 231.90397
2016-10 9.433894 2161.20 229.08885
2016-11 9.388671 2111.72 224.92214
2016-12 9.391744 2191.08 233.29853
2017-1 9.518196 2257.83 237.21197
2017-2 9.486474 2279.55 240.29476
2017-3 9.473612 2395.96 252.90880
2017-4 9.553290 2358.84 246.91389
2017-5 9.478851 2388.33 251.96408
2017-6 9.456445 2430.06 256.97395
2017-7 9.542871 2429.01 254.53661
2017-8 9.498936 2476.35 260.69764
2017-9 9.590886 2476.55 258.21911
2017-10 9.636859 2529.12 262.44237
2017-11 9.595464 2579.36 268.81035
2017-12 9.621027 2642.22 274.62972
2018-1 9.704813 2695.81 277.78072
2018-2 9.706801 2821.98 290.72194
2018-3 9.728749 2677.67 275.23270
2018-4 9.820276 2581.88 262.91317
2018-5 9.744355 2654.80 272.44492
2018-6 9.717360 2734.62 281.41596
2018-7 9.792442 2726.71 278.45045
2018-8 9.776606 2813.36 287.76451
2018-9 9.926425 2896.72 291.81906
2018-10 9.890615 2924.59 295.69344
2018-11 9.931924 2740.37 275.91532
2018-12 10.037999 2790.37 277.98070
2019-1 10.057138 NA NA

3.5 CEO-to-worker Compensation Ratio

And this precipitous increase in corporate profits and valuation, having not trickled down to the middle class, appears to have mostly gone to the corporate executives of America. While in the 60s and early 70s, a CEO only earned about 20x to 25x the amount their worker did, this ratio skyrocketed to more 320 by 2019. This change may not have taken place dramatically in the year 1971, but it can be argued that the decade following ’71 was a long process of trend reversal from relative parity to the outsized ratio of modern America.

3.5.1 Plot

3.5.2 Code

plot_ceo_ratio <- read_csv("data/ceo_comp.csv") %>% 
  mutate(
    realized = realized %>% str_extract("^\\d+.{0,1}\\d+") %>% as.numeric()
  ) %>% 
  ggplot(
    aes(
      x = year,
      y = realized
    )
  )+
  geom_smooth(col   = palette_prim[2],
              fill  = palette_2nd[2],
              size  = 1.6,
              alpha = .75)+
  geom_line(col  = palette_prim[3],
            size = 1.25)+
  geom_vline(
    xintercept = 1971,
    linetype = "dashed",
    size = 0.75,
    col = palette_grid
  )+
  theme_pelican70()+
  labs(
    title = "CEO-worker compensation ratio 1965-2019",
    subtitle = "(with Loess smoothed trendline)",
    x = "Year",
    y = "Ratio\n",
    caption = "Data Source: Economic Policy Institute”"
  ) +
  annotate(
    "text",
    x = 1969,
    y = 350,
    label = "1971",
    fontface = 2,
    col = palette_grid
  ) +
  theme(legend.position = "none")

ggsave(plot = plot_ceo_ratio,
       filename = "plots/plot_ceo_ratio.png",
       h = 5,
       w = 10,
       type = "cairo-png")

3.5.3 Data

Year Realized Compensation Ratio
1965 21.1
1966 22.3
1967 23.6
1968 24.8
1969 24.6
1970 24.3
1971 24.0
1972 23.7
1973 23.4
1974 25.0
1975 26.6
1976 28.2
1977 29.8
1978 31.4
1979 34.1
1980 36.9
1981 39.6
1982 42.3
1983 45.0
1984 47.8
1985 50.5
1986 53.2
1987 55.9
1988 58.7
1989 61.4
1990 77.3
1991 93.1
1992 109.0
1993 108.6
1994 87.4
1995 117.6
1996 150.6
1997 223.4
1998 297.4
1999 266.1
2000 365.7
2001 210.6
2002 186.8
2003 228.8
2004 265.7
2005 318.4
2006 328.2
2007 330.9
2008 206.7
2009 177.6
2010 213.1
2011 242.4
2012 371.7
2013 318.5
2014 326.6
2015 318.8
2016 271.6
2017 302.1
2018 293.3
2019 320.0

3.6 Inequality 1: Gini Index

Unsurprisingly this previous pattern has corresponded with an increase in the Gini Index, measuring household income inequality. Here it is clear through the smoothed trendline that the years around 1971 were the bottom of the valley that signalled a trend reversal towards the massive inequality in modern America.

Definition of the Gini Index, from the OECD:

The Gini index measures the extent to which the distribution of income (or, in some cases, consumption expenditure) among individuals or households within an economy deviates from a perfectly equal distribution.

The Gini index measures the area between the Lorenz curve and the hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line.

A Gini index of zero represents perfect equality and 1.00, perfect inequality.

3.6.1 Plot

3.6.2 Code

gini <- fredr(
  series_id = "GINIALLRF",
  observation_start = as.Date("1800-01-01"),
  observation_end = as.Date(Sys.Date())
) 

plot_gini <- gini %>% 
  mutate(date = year(date)) %>% 
  ggplot(
    aes(
      x = date,
      y = value
    )
  ) + 
  geom_smooth(col   = palette_prim[2],
              fill  = palette_2nd[2],
              size  = 1.25,
              alpha = .75)+
  geom_line(col  = palette_prim[3],
            size = 1.25)+
  geom_vline(
    xintercept = 1971,
    linetype = "dashed",
    size = 0.75,
    col = palette_grid
  )+
  theme_pelican70()+
  labs(
    title = "Gini Index of Household Income Inequality 1947-2018",
    subtitle = "(with Loess smoothed trendline)",
    x = "Year",
    y = "Gini Coefficient \n",
    caption = "Data Source: FRED"
  ) +
  annotate(
    "text",
    x = 1969,
    y = 0.46,
    label = "1971",
    fontface = 2,
    col = palette_grid
  ) +
  
  theme(legend.position = "none")

ggsave(plot = plot_gini,
       filename = "plots/plot_gini.png",
       h = 5,
       w = 10,
       type = "cairo-png")

3.6.3 Data

Year Gini Coeffcient
1947 0.376
1948 0.371
1949 0.378
1950 0.379
1951 0.363
1952 0.368
1953 0.359
1954 0.371
1955 0.363
1956 0.358
1957 0.351
1958 0.354
1959 0.361
1960 0.364
1961 0.374
1962 0.362
1963 0.362
1964 0.361
1965 0.356
1966 0.349
1967 0.358
1968 0.348
1969 0.349
1970 0.353
1971 0.355
1972 0.359
1973 0.356
1974 0.355
1975 0.357
1976 0.358
1977 0.363
1978 0.363
1979 0.365
1980 0.365
1981 0.369
1982 0.380
1983 0.382
1984 0.383
1985 0.389
1986 0.392
1987 0.393
1988 0.395
1989 0.401
1990 0.396
1991 0.397
1992 0.404
1993 0.429
1994 0.426
1995 0.421
1996 0.425
1997 0.429
1998 0.430
1999 0.429
2000 0.433
2001 0.435
2002 0.434
2003 0.436
2004 0.438
2005 0.440
2006 0.444
2007 0.432
2008 0.438
2009 0.443
2010 0.440
2011 0.450
2012 0.451
2013 0.455
2014 0.452
2015 0.448
2016 0.452
2017 0.458
2018 0.452
2019 0.454

3.7 Inequality 2: Top 10% as share of total income

In a similar pattern, while in the post-war era, the top 10% of earners in America accounted for an unprecedentedly small proportion of total income, this proportion started to increase dramatically again, with the trend reversal beginning around 1971. Data here is taken from Saez and Piketty’s original research.

3.7.1 Plot

3.7.2 Code

plot_top10_incomeshare <- read_csv("data/fct_income_distro.csv") %>% 
  select(year,top10) %>% 
  pivot_longer(
    cols = -year,
    names_to = "pop",
    values_to = "share"
  ) %>% 
  ggplot(
    aes(
      x = year,
      y = share,
      col = pop
    )
  )+
  geom_smooth(col   = palette_prim[2],
              fill  = palette_2nd[2],
              size  = 1.6,
              alpha = .75)+
  geom_line(size = 1.25,
            col = palette_prim[3])+
  geom_vline(
    xintercept = 1971,
    linetype = "dashed",
    size = 0.75,
    col = palette_grid
  )+
  theme_pelican70()+
  scale_x_continuous(breaks = seq(1900,2020,20))+
  scale_y_continuous(labels = scales::percent)+
  labs(
    title = "Share of total income by top 10% earners 1917-1998",
    subtitle = "(with Loess smoothed trendline)",
    x = "Year",
    y = "% Share of total \n",
    caption = "Data Source: Piketty and Saez, 2003”"
  ) +
  annotate(
    "text",
    x = 1968,
    y = 0.49,
    label = "1971",
    fontface = 2,
    col = palette_grid
  ) +
  theme(legend.position = "none")

ggsave(plot = plot_top10_incomeshare,
       filename = "plots/plot_top10_incomeshare.png",
       h = 5,
       w = 10,
       type = "cairo-png")

3.7.3 Data

Year Top 10% Top 5% Top 1% Top 0.5% Top 0.1% Top 0.01%
1913 0.4333371 0.3305022 0.2053119 0.1744347 0.0923486 0.0317253
1914 0.4394441 0.3373522 0.2102100 0.1796748 0.0936562 0.0346043
1915 0.4321828 0.3298351 0.2037836 0.1744433 0.0968058 0.0428369
1916 0.4460024 0.3499577 0.2145887 0.1806900 0.1140153 0.0495340
1917 0.4517593 0.3597944 0.2140942 0.1746333 0.1026482 0.0395196
1918 0.4424876 0.3462763 0.1979529 0.1552008 0.0841323 0.0288916
1919 0.4617415 0.3712198 0.2172954 0.1692353 0.0893450 0.0289547
1920 0.4439024 0.3453164 0.1922266 0.1449608 0.0690709 0.0190420
1921 0.4756666 0.3604981 0.1927004 0.1455222 0.0695543 0.0191110
1922 0.4630841 0.3509109 0.1867308 0.1414222 0.0688050 0.0210190
1923 0.4393464 0.3323454 0.1779217 0.1353732 0.0657626 0.0197157
1924 0.4594547 0.3459150 0.1856044 0.1414393 0.0689394 0.0207939
1925 0.4745530 0.3692036 0.2076695 0.1591498 0.0805440 0.0274063
1926 0.4783624 0.3781228 0.2198594 0.1699015 0.0895978 0.0321015
1927 0.4732464 0.3721427 0.2131644 0.1640334 0.0859137 0.0308086
1928 0.4844467 0.3821951 0.2236085 0.1742246 0.0956928 0.0370300
1929 0.4716386 0.3758773 0.2224790 0.1744123 0.0987616 0.0405552
1930 0.4654305 0.3554737 0.1943886 0.1470222 0.0753468 0.0266947
1931 0.4660781 0.3391211 0.1681897 0.1230676 0.0583588 0.0185423
1932 0.4858131 0.3544893 0.1655634 0.1229813 0.0585266 0.0149866
1933 0.4827451 0.3624026 0.1751608 0.1312048 0.0646577 0.0185968
1934 0.4932814 0.3814154 0.1911131 0.1455317 0.0713578 0.0213557
1935 0.4831617 0.3679803 0.1938906 0.1483617 0.0737502 0.0226430
1936 0.4860467 0.3731407 0.2097519 0.1615526 0.0792884 0.0230865
1937 0.4772440 0.3650566 0.2070979 0.1588284 0.0785776 0.0237167
1938 0.4733016 0.3508255 0.1868631 0.1404268 0.0688876 0.0234690
1939 0.4882407 0.3634298 0.1960684 0.1475756 0.0713397 0.0215798
1940 0.4911092 0.3713569 0.2089458 0.1594255 0.0796649 0.0260271
1941 0.4715121 0.3661888 0.2159736 0.1652387 0.0839767 0.0283625
1942 0.4254670 0.3368301 0.2062793 0.1593192 0.0803333 0.0252391
1943 0.3916021 0.3109285 0.1865804 0.1410814 0.0682211 0.0185650
1944 0.3631141 0.2755470 0.1541485 0.1139722 0.0533749 0.0163133
1945 0.3557713 0.2686349 0.1432875 0.1031265 0.0455017 0.0121387
1946 0.3722210 0.2823528 0.1432552 0.1009792 0.0431305 0.0125581
1947 0.3713386 0.2858498 0.1502405 0.1080299 0.0496726 0.0161372
1948 0.3906762 0.3016048 0.1635261 0.1202975 0.0568377 0.0180546
1949 0.3853625 0.2924830 0.1573189 0.1159369 0.0552045 0.0179624
1950 0.3922462 0.3009263 0.1674159 0.1229772 0.0595827 0.0151546
1951 0.3811005 0.2920847 0.1604361 0.1170173 0.0550234 0.0178189
1952 0.3685850 0.2786406 0.1499114 0.1091833 0.0513007 0.0162292
1953 0.3588413 0.2674690 0.1405794 0.1020816 0.0475397 0.0155246
1954 0.3615750 0.2687355 0.1393599 0.0999914 0.0466189 0.0145659
1955 0.3687712 0.2750502 0.1461809 0.1056338 0.0515806 0.0172474
1956 0.3586891 0.2654753 0.1379306 0.1016344 0.0480587 0.0154452
1957 0.3582972 0.2652427 0.1349883 0.0986901 0.0458349 0.0141702
1958 0.3564746 0.2592521 0.1261834 0.0907839 0.0407342 0.0123379
1959 0.3607414 0.2645023 0.1314602 0.0957372 0.0433264 0.0132543
1960 0.3562313 0.2574691 0.1261439 0.0911476 0.0427853 0.0145803
1961 0.3569859 0.2573828 0.1244148 0.0894055 0.0424586 0.0147527
1962 0.3613481 0.2604818 0.1261647 0.0911020 0.0426696 0.0144227
1963 0.3624446 0.2615898 0.1266419 0.0917370 0.0434188 0.0149748
1964 0.3635411 0.2626977 0.1271192 0.0923720 0.0441680 0.0155269
1965 0.3599449 0.2601079 0.1263451 0.0921294 0.0444865 0.0157032
1966 0.3563487 0.2575181 0.1255709 0.0918868 0.0448050 0.0158796
1967 0.3522355 0.2536897 0.1226331 0.0891639 0.0424676 0.0145031
1968 0.3488327 0.2501628 0.1201499 0.0871808 0.0413171 0.0140288
1969 0.3399681 0.2404240 0.1125239 0.0809116 0.0377438 0.0129499
1970 0.3370734 0.2357901 0.1073542 0.0762765 0.0347541 0.0117322
1971 0.3403034 0.2380933 0.1081123 0.0767928 0.0349805 0.0117191
1972 0.3424716 0.2395630 0.1079242 0.0763070 0.0345885 0.0114937
1973 0.3419868 0.2388404 0.1059334 0.0742853 0.0332759 0.0107693
1974 0.3389170 0.2349509 0.1032441 0.0722258 0.0321063 0.0103596
1975 0.3406429 0.2351802 0.1030850 0.0719333 0.0320173 0.0105714
1976 0.3412797 0.2354719 0.1030641 0.0720724 0.0322211 0.0107325
1977 0.3419326 0.2361789 0.1034741 0.0725494 0.0325412 0.0108807
1978 0.3409258 0.2354379 0.1034481 0.0729761 0.0332592 0.0113280
1979 0.3425164 0.2380716 0.1067121 0.0762357 0.0360266 0.0128225
1980 0.3401733 0.2337849 0.1027903 0.0728970 0.0337117 0.0115469
1981 0.3441539 0.2368951 0.1049075 0.0747511 0.0349213 0.0120825
1982 0.3485344 0.2397379 0.1076202 0.0779525 0.0388906 0.0152782
1983 0.3545140 0.2441762 0.1110138 0.0809154 0.0409097 0.0172215
1984 0.3596841 0.2501457 0.1154284 0.0851024 0.0436300 0.0180630
1985 0.3611107 0.2512243 0.1173539 0.0867153 0.0448796 0.0183161
1986 0.3597864 0.2485901 0.1137119 0.0832807 0.0414024 0.0168528
1987 0.3700553 0.2606897 0.1246581 0.0925761 0.0476849 0.0204081
1988 0.3856257 0.2778675 0.1413977 0.1078970 0.0579889 0.0241027
1989 0.3820587 0.2738112 0.1370576 0.1035599 0.0545000 0.0228601
1990 0.3832241 0.2742996 0.1376733 0.1038890 0.0543063 0.0226277
1991 0.3829378 0.2717450 0.1316371 0.0980381 0.0503419 0.0207310
1992 0.3944372 0.2830691 0.1413139 0.1064086 0.0563515 0.0236993
1993 0.3915791 0.2793503 0.1361625 0.1016088 0.0531196 0.0221129
1994 0.3906146 0.2781474 0.1347971 0.1002252 0.0519671 0.0214973
1995 0.3979319 0.2850787 0.1402758 0.1050196 0.0550491 0.0227894
1996 0.4061743 0.2944113 0.1475645 0.1113264 0.0597775 0.0255625
1997 0.4128095 0.3017584 0.1544617 0.1177506 0.0642970 0.0277916
1998 0.4166779 0.3059773 0.1586918 0.1216574 0.0671484 0.0286588
1999 0.4206951 0.3098797 0.1623893 0.1252473 0.0701270 0.0304722
2000 0.4265723 0.3162443 0.1695275 0.1320947 0.0758194 0.0338332
2001 0.4202892 0.3090904 0.1624824 0.1255752 0.0703299 0.0304399
2002 0.4170105 0.3048225 0.1579016 0.1210236 0.0671813 0.0294677
2003 0.4192948 0.3067952 0.1599950 0.1232462 0.0693175 0.0312881
2004 0.4251735 0.3136402 0.1666804 0.1292530 0.0732875 0.0325762
2005 0.4342811 0.3233857 0.1748806 0.1366006 0.0785479 0.0343092
2006 0.4421310 0.3308800 0.1810527 0.1421320 0.0820945 0.0358069
2007 0.4421775 0.3316491 0.1827772 0.1440801 0.0842070 0.0383803
2008 0.4417405 0.3290365 0.1787328 0.1405258 0.0826122 0.0385976
2009 0.4358040 0.3189771 0.1668946 0.1294684 0.0740768 0.0333726
2010 0.4462403 0.3305499 0.1769001 0.1381596 0.0795252 0.0348214
2011 0.4528851 0.3373202 0.1823434 0.1428733 0.0829748 0.0379600
2012 0.4624456 0.3480668 0.1927508 0.1521953 0.0894757 0.0408309
2013 0.4563442 0.3403918 0.1834086 0.1432901 0.0835862 0.0380791
2014 0.4613220 0.3458798 0.1883998 0.1474539 0.0857489 0.0390228
2015 0.4629940 0.3467692 0.1881906 0.1473641 0.0854856 0.0387437
2016 0.4617064 0.3445860 0.1858382 0.1450613 0.0838123 0.0373327
2017 0.4590072 0.3439488 0.1865074 0.1462892 0.0855982 0.0402410
2018 0.4594392 0.3448937 0.1877627 0.1471784 0.0860407 0.0401177
2019 0.4584145 0.3434592 0.1860017 0.1453777 0.0842893 0.0383910